It is known that Gaussian Mixture Models (GMMs) continue to be extremely popular for recognition-type problems in speech. While GMMs allow for fast model training and scoring, training samples are pooled together for parameter estimation, resulting in a loss of information that exists within individual training samples.
At the other extreme, exemplar-based techniques utilize information about actual training examples. While exemplar-based methods have been shown to offer improvements in accuracy over GMMs for classification tasks, this is not the case with recognition tasks. As is known, speech classification is the task of classifying a speech signal as a given class or type from among a given set of classes or types known a priori, while speech recognition is the task of decoding the speech signal to generate recognition results. It is to be understood though that speech classification can be performed in the speech recognition task but, in such case, classes or types are typically not known a priori.